Cardiovascular disease (CVD) is a leading cause of global mortality, demanding accurate and interpretable risk stratification models for early intervention. However, the tradeoff between model performance and clinical interpretability remains a gap. Therefore, the research evaluates lightweight parametric ensemble models against complex black-box alternatives. A comparative analysis of Logistic Regression (LR) and Gaussian Naive Bayes (GNB) is conducted using a publicly available dataset of 1,000 patient records, enhanced with Bagging and AdaBoost ensemble methods. The data undergo standardized preprocessing to mitigate bias, and a fivefold stratified cross-validation protocol ensures model generalizability. The Bagging LR ensemble achieves the best predictive performance, with a mean accuracy of 0.966 (95% Confidence Interval (CI): [0.958, 0.974]) and a Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) of 0.993 (95% CI: [0.990, 0.996]), highly competitive with state-of-the-art baselines including LightGBM and CatBoost. These results show that computationally efficient and interpretable ensembles provide a viable alternative to more complex models, particularly in data-constrained clinical settings. A comprehensive Explainable Artificial Intelligence (XAI) analysis using SHapley Additive exPlanations (SHAP) identifies clinically congruent drivers of prediction, the slope of the peak exercise ST segment, ST depression, and chest pain type. The findings highlight the potential of interpretable ensemble learning to develop dependable, deployable clinical decision support tools for integration into hospital or e-health systems for early CVD risk stratification. Further validation on larger, more diverse datasets is recommended to confirm broader clinical applicability.
Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.